CN117172523A - Risk division method, device, equipment and medium based on vehicle safety - Google Patents
Risk division method, device, equipment and medium based on vehicle safety Download PDFInfo
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Abstract
The application relates to the field of data processing, and discloses a risk dividing method based on vehicle safety. According to the application, the risk factors possibly appearing are evaluated by combining the data of the vehicle, the traffic safety data, the artificial risk data and the like, and the risk level can be accurately evaluated from multiple aspects in the process of classifying the risk levels according to the preset data and monitoring the behaviors of the vehicle and the driver in real time to judge whether the vehicle and the driver have risks or not, so that potential traffic accidents are effectively avoided.
Description
Technical Field
The application relates to the field of data processing, in particular to a risk dividing method, device, equipment and medium based on vehicle safety.
Background
Along with the rapid development of social economy and the rapid promotion of urban progress, automobiles become important transportation means for production and living of people, and accident frequency is caused by complex and changeable traffic environment in the transportation operation process, so that the driving safety is particularly important.
In the prior art, when determining the driving risk of a vehicle, it is common to use only GPS data of the vehicle or only vehicle insurance data. In addition, when judging the driving risk of the vehicle, the risk cannot be classified accurately, so that there may be an inaccurate problem in judging the risk of the vehicle.
Disclosure of Invention
The application mainly aims to provide a method, a device, equipment and a medium for dividing risks based on vehicle safety, which aim to solve the technical problems that in the prior art, risk grades are not evaluated in multiple aspects and risk quantification is not accurate enough.
In order to achieve the above object, the present application provides a risk dividing method based on vehicle safety, comprising:
acquiring vehicle data;
the vehicle data are uniformly arranged and stored;
judging whether risks exist according to the tidied vehicle data;
and if the risk exists, classifying the risk grade.
Further, the step of acquiring vehicle data includes:
acquiring vehicle data based on a multi-source acquisition method;
the vehicle data includes at least vehicle itself data, vehicle travel preset line data, and driver behavior monitoring data.
Further, the step of uniformly sorting and storing the vehicle data includes:
carrying out data standardization processing on the vehicle data to obtain standardized data;
and uniformly storing the standardized data.
Further, the step of judging whether there is a risk according to the collated vehicle data includes:
analyzing the vehicle data;
judging whether risk exists according to the analysis result.
Further, the step of classifying the risk if the risk exists includes:
acquiring vehicle physical attribute data and vehicle state data based on vehicle own data;
detecting whether the reject ratio of the vehicle physical attribute data and the vehicle state data exceeds a threshold value;
if the threshold value is exceeded, judging that the risk of the vehicle exists;
and classifying the risk grades according to the preset risk grades of the vehicles.
Further, the step of classifying the risk if the risk exists includes:
acquiring the current position of the vehicle based on the positioning device;
acquiring vehicle running preset line data based on navigation information;
detecting whether an unexpected event is found on a running preset line of the vehicle;
if an accident occurs, judging that traffic safety risks exist;
and classifying the risk levels according to the preset risk levels of the accidents.
Further, the step of classifying the risk if the risk exists includes:
acquiring driver behavior monitoring data in real time based on a face recognition device;
judging whether abnormal behaviors occur to the driver behavior monitoring data;
if abnormal behaviors occur, judging that artificial risks exist;
and classifying the risk grades according to the preset abnormal behavior grades of the driver.
The application also provides a risk dividing device based on vehicle safety, which is characterized by comprising the following steps:
the acquisition module is used for: for acquiring vehicle data;
and (3) a finishing module: the vehicle data are uniformly arranged and stored;
and a judging module: judging whether risks exist according to the tidied vehicle data;
the dividing module: for classifying risk if there is a risk.
The application also provides a computer device which comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method when executing the computer program.
The application also proposes a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the above-mentioned method.
The application discloses a risk dividing method based on vehicle safety, which comprises the steps of acquiring vehicle data, uniformly arranging and storing the data, evaluating factors possibly generating risks, judging whether risks exist according to the arranged vehicle data, determining risk categories such as personnel or property possibly involved, searching personnel, equipment and other information possibly exposed to the risks, and finally quantifying risk dividing grades. According to the application, the risk factors possibly appearing are evaluated by combining the data of the vehicle, the traffic safety data, the artificial risk data and the like, and the risk level can be accurately evaluated from multiple aspects in the process of classifying the risk levels according to the preset data and monitoring the behaviors of the vehicle and the driver in real time to judge whether the vehicle and the driver have risks or not, so that potential traffic accidents are effectively avoided.
Drawings
FIG. 1 is a schematic diagram showing steps of a risk classification method based on vehicle safety according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a vehicle safety-based risk division architecture according to an embodiment of the present application;
FIG. 3 is a block diagram of a computer device according to one embodiment of the application;
the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, modules, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any module and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1, an embodiment of the present application provides a risk dividing method based on vehicle safety, including the following steps S1 to S3:
s1: acquiring vehicle data;
for step S1, the data of the vehicle includes, but is not limited to, data of physical properties of the vehicle, data of status of the vehicle, data of a vehicle bill, data of basic properties of a driver, data of monitoring driving behavior, data of intelligent positioning device, data of warning, etc. The method for acquiring the data can be acquired according to a database of the vehicle, wherein the database can be a database in a central control server of the vehicle or a server database which is connected with middle-end equipment through a network and stores normal driving state information; or can be through the middle end apparatus used for gathering the vehicle status data on vehicles such as the car subsides containing various sensors; or can acquire real-time state data and real-time driver behavior data of the vehicle based on the wireless communication network; or the state information of the vehicle can be obtained through various sensors in the intermediate end equipment, such as a speed sensor, an acceleration sensor, a pressure sensor, a gyroscope and the like, and comprises vehicle data when the vehicle is stopped, various driving data in the driving process of the vehicle, such as speed information, collision information, line crossing information, lane changing information, start-stop information and the like when the vehicle is driven, and passive collision information and the like when the vehicle is stopped.
S2: the vehicle data are uniformly arranged and stored;
for step S2, data sorting includes the processes of data cleansing, data conversion, classification coding, and digital coding, where data cleansing occupies the most important position, namely, checking data consistency, and processing invalid values and missing values. The data arrangement is a process of cleaning, organizing and converting the original data into a required format, and the data can be uniformly arranged and stored by establishing a risk database and a multi-source acquisition and storage method. The unified data can be converted into a compatible format to improve usability, a large amount of data can be easily processed, noise, defects and missing elements in the data can be removed, and accurate judgment and analysis can be timely performed on the large amount of data.
S3: judging whether risks exist according to the tidied vehicle data;
and step S3, evaluating factors such as natural conditions, working environments, staff behaviors, external personnel and the like which possibly generate risks in specific business matters such as vehicles and vehicles in the running process, traffic safety, artificial risks and the like, and determining whether the risks such as possibly related persons, properties and other risks exist according to the sorted data. The method can judge the current state of the vehicle to be tested based on the basic information of the vehicle to be tested, the driving information of the vehicle and the maintenance information of the vehicle, and judge whether the vehicle to be tested has safety risk to the target vehicle based on the current state of the vehicle and the bad information in the traffic information. It should be noted that whether the vehicle to be tested has a safety risk to the target vehicle can be judged according to the current condition of the vehicle alone or according to the bad information in the traffic information alone. Specifically, if the number of times of vehicle maintenance exceeds a threshold value, the risk of the vehicle exists, and if the threshold value is more than 5, the risk of the vehicle is judged if the number of times of vehicle maintenance is obtained according to the sorted data and exceeds the threshold value. Or the navigation information of the vehicle can be acquired based on the intelligent positioning device, and whether the vehicle has safety risk or not is judged through the current condition of the vehicle and the traffic record.
S4: if the risk exists, classifying the risk grade;
for step S4, vehicle risk level information (for example, a vehicle risk level table) may be determined according to a certain policy according to the total vehicle risk score of the vehicle risk scheme, the total vehicle risk score is used as an upper score limit, and the vehicle risk level information is generated according to the number of the vehicle risk levels and the proportion occupied by the vehicle risk levels, and then the vehicle risk level of the vehicle risk object is determined by searching the vehicle risk level table. For example, the human risk level is determined, the actions of the driver can be monitored based on the face recognition device, and the risk score types can be set to be suspected fatigue, distraction, illegal abnormality and collision risk respectively. It is possible to determine which actions are the above kinds based on the detection of the driver actions by the face recognition device. If the suspected fatigue is judged, judging whether the driver is yawed or closed, wherein the risk interval time for judging the suspected fatigue is set to be 60s; if the attention is dispersed, whether the driver smokes, calls a hand-held phone, and does not see the front for a long time can be judged, wherein the risk interval time for judging the attention dispersed is set to be 30s; if the rule violation is judged, judging whether the driver is not in the driving position, the tire pressure, the overspeed, the shielding, the infrared blocking and the simultaneous separation of the two hands from the steering wheel, wherein the risk interval time of the rule violation is set to be 20s; if the collision risk is judged, it is possible to judge whether the driver has a vehicle forward collision, lane departure, too close a vehicle distance, a pedestrian collision, frequent lane change, a blind area collision, sudden acceleration, sudden deceleration, a sudden turn, or the like, wherein the risk interval time for judging the collision risk is set to 50s. Each risk score category is respectively set with a score, such as yawning, smoking and the like, as 1 score, and vehicle risk scores are set to be more than 2 scores and are divided into primary risks, and vehicle risk scores are set to be more than 3 scores and are divided into secondary risks and the like. Preferably, various methods can be employed to systematically categorize the potential and various risks that have not yet occurred, to sum up the risks faced by the vehicle and to analyze the potential causes of the risk accident. The risk level is accurately divided, and then corresponding measures can be better adopted to avoid risks according to the risk level, so that the risk is prevented.
In this embodiment, factors that may generate risks are evaluated by acquiring vehicle data and uniformly sorting and storing the data, then whether risks exist or not is judged according to the sorted vehicle data, risk categories such as persons or properties that may be involved are determined, personnel, equipment and other information that may be exposed to the risks are searched, and finally the risk classification is quantified. According to the application, a plurality of data such as vehicle self data, traffic safety data, artificial risk data and the like are combined to evaluate possible risk factors, and in the process of classifying risk grades according to preset data, monitoring the behaviors of the vehicle and the driver in real time to judge whether the risks exist in the vehicle and the driver, the existing risks must be analyzed and classified before the risks are controlled, so that the risk grades can be accurately evaluated from multiple aspects, and potential traffic accidents are effectively avoided.
In one embodiment of the application, vehicle data is acquired based on a multi-source acquisition method;
the vehicle data includes at least vehicle itself data, vehicle travel preset line data, and driver behavior monitoring data.
In this embodiment, the data of the vehicle includes, but is not limited to, data of the vehicle itself, data of a preset line of travel of the vehicle, data of a vehicle bill, data of basic attributes of a driver, data of driving behavior monitoring, data of an intelligent positioning device, data of an alarm, and the like. The vehicle data comprises vehicle physical attribute data and vehicle state data, the vehicle data is hardware data of the vehicle, and the vehicle data can be used for acquiring middle-end equipment of the hardware data of the vehicle on the vehicle such as a vehicle subsides of various sensors. The vehicle data acquisition method based on the multi-source acquisition is more comprehensive and accurate. The majority of the data acquired from the vehicle may be acquired through a database of the vehicle, which may be a database in a vehicle central control server; the system can also be a server database which is connected with the middle-end equipment through a network and stores normal driving state information; or can be through the middle end apparatus used for gathering the vehicle status data on vehicles such as the car subsides containing various sensors; or can acquire real-time state data and real-time driver behavior data of the vehicle based on the wireless communication network; or the state information of the vehicle can be obtained through various sensors in the intermediate end equipment, such as a speed sensor, an acceleration sensor, a pressure sensor, a gyroscope and the like, and comprises vehicle data when the vehicle is stopped, various driving data in the driving process of the vehicle, such as speed information, collision information, line crossing information, lane changing information, start-stop information and the like when the vehicle is driven, and passive collision information and the like when the vehicle is stopped.
In an embodiment of the present application, the step of uniformly sorting and storing the vehicle data includes:
carrying out data standardization processing on the vehicle data to obtain standardized data;
and uniformly storing the standardized data.
In this embodiment, the data sorting includes the processes of data cleansing, data conversion, classification coding, and digital coding, where data cleansing occupies the most important position, namely, checking data consistency, and processing invalid values and missing values. The data arrangement is a process of cleaning, organizing and converting the original data into a required format, and the data can be uniformly arranged and stored by establishing a risk database and a multi-source acquisition and storage method. The unified arrangement and storage of the data can convert the data into a compatible format to improve the usability, can easily process a large amount of data, can remove noise, defects and missing elements in the data, and can timely and accurately judge and analyze the large amount of data.
In an embodiment of the present application, the step of determining whether there is a risk according to the sorted vehicle data includes:
analyzing the vehicle data;
judging whether risk exists according to the analysis result.
In this embodiment, the risk of the vehicle and the specific business matters such as the vehicle itself, traffic safety, and artificial risk during the running process is evaluated, and whether the risk of the person, property, and other related risks possibly exists is determined according to the data collected. The method can judge the current state of the vehicle to be tested based on the basic information of the vehicle to be tested, the driving information of the vehicle and the maintenance information of the vehicle, and judge whether the vehicle to be tested has safety risk to the target vehicle based on the current state of the vehicle and the bad information in the traffic information. It should be noted that whether the vehicle to be tested has a safety risk to the target vehicle can be judged according to the current condition of the vehicle alone or according to the bad information in the traffic information alone. Specifically, if the number of times of vehicle maintenance exceeds a threshold value, the risk of the vehicle exists, and if the threshold value is more than 5, the risk of the vehicle is judged if the number of times of vehicle maintenance is obtained according to the sorted data and exceeds the threshold value. Or the navigation information of the vehicle can be acquired based on the intelligent positioning device, and whether the vehicle has safety risk or not is judged through the current condition of the vehicle and the traffic record.
In an embodiment of the present application, the step of classifying the risk if the risk exists includes:
acquiring vehicle physical attribute data and vehicle state data based on vehicle own data;
detecting whether the reject ratio of the vehicle physical attribute data and the vehicle state data exceeds a threshold value;
if the threshold value is exceeded, judging that the risk of the vehicle exists;
and classifying the risk grades according to the preset risk grades of the vehicles.
In this embodiment, the vehicle risk level information (for example, may be a vehicle risk level table) may be determined according to a certain policy according to the total vehicle risk score of the vehicle risk scheme, the total vehicle risk score is used as an upper score limit, and the vehicle risk level information is generated according to the number of the vehicle risk levels and the proportion occupied by the vehicle risk levels, and then the vehicle risk level of the vehicle risk object is determined by searching the vehicle risk level table. The present condition of the vehicle can be judged based on the basic information of the vehicle, the driving information of the vehicle and the maintenance information of the vehicle, and whether the vehicle to be tested has safety risk to the target vehicle or not can be judged based on the present condition of the vehicle and the bad information in the traffic information. It should be noted that whether the vehicle to be tested has a safety risk to the target vehicle can be judged according to the current condition of the vehicle alone or according to the bad information in the traffic information alone. Such as detecting the failure rate of the vehicle by various sensors in the intermediate-end device or by a vehicle database. If the defective rate of the vehicle is detected through maintenance records (vehicle health condition), if the maintenance frequency threshold is set to be 3, the maintenance score 1 is set when the maintenance frequency is 3, if the maintenance frequency exceeds 3 times and does not exceed 1 time, a score is added, and the risk of the vehicle is judged. If the tire pressure of the vehicle can be set, the maximum air pressure is set to be 3.0bar, and when the air pressure is set to be 1 at the other position, the score of 1 is added when the air pressure is not more than 0.5 bar. The risk grades of the preset vehicles can be divided into a first grade, a second grade and a third grade, the risk scores of the first grade, the second grade and the third grade are respectively set to be 3 minutes, 4 minutes and 5 minutes, if the maintenance times are 4 times and the tire pressure is 3.5bar, the risk score of the vehicle is 4 minutes at the moment, the risk of the second grade is achieved, and the risk grade is divided into the second grade at the moment.
In an embodiment of the present application, the step of classifying the risk if the risk exists includes:
acquiring the current position of the vehicle based on the positioning device;
acquiring vehicle running preset line data based on navigation information;
detecting whether an unexpected event is found on a running preset line of the vehicle;
if an accident occurs, judging that traffic safety risks exist;
and classifying the risk levels according to the preset risk levels of the accidents.
In this instance, in the present embodiment, the vehicle positioning device may be at least one of a plurality of mobile terminals for acquiring the position information of the corresponding vehicle itself. For example, the vehicle positioning device may include a car navigation device, a car positioning device, a mobile device such as a cell phone, and the like. The vehicle positioning device can collect positioning information of the target vehicle in the driving process. The accident may evaluate a risk of collision between the target vehicle and other vehicles around based on the target vehicle location information. Or judging whether the weather is bad or not according to whether traffic accidents occur on a preset route of a user or according to the current weather, so that unexpected events such as mountain slides and the like can be caused. If stormwater weather or traffic 10 km in front is set as a first-level traffic safety risk, mountain sliding waves 5 km in front is set as a second-level traffic safety risk, and front bridge collapse is set as a third-level traffic safety risk and the like. The risk level may be classified according to the risk level of the accident, and if the traffic safety risk is secondary, the risk level is secondary.
In an embodiment of the present application, the step of classifying the risk if the risk exists includes:
acquiring driver behavior monitoring data in real time based on a face recognition device;
judging whether abnormal behaviors occur to the driver behavior monitoring data;
if abnormal behaviors occur, judging that artificial risks exist;
and classifying the risk grades according to the preset abnormal behavior grades of the driver.
In this embodiment, the vehicle risk level information (for example, may be a vehicle risk level table) may be determined according to a certain policy according to the total vehicle risk score of the vehicle risk scheme, the total vehicle risk score is used as an upper score limit, and the vehicle risk level information is generated according to the number of the vehicle risk levels and the proportion occupied by the vehicle risk levels, and then the vehicle risk level of the vehicle risk object is determined by searching the vehicle risk level table. For example, the human risk level is determined, the actions of the driver can be monitored based on the face recognition device, and the risk score types can be set to be suspected fatigue, distraction, illegal abnormality and collision risk respectively. It is possible to determine which actions are the above kinds based on the detection of the driver actions by the face recognition device. If the suspected fatigue is judged, judging whether the driver is yawed or closed, wherein the risk interval time for judging the suspected fatigue is set to be 60s; if the attention is dispersed, whether the driver smokes, calls a hand-held phone, and does not see the front for a long time can be judged, wherein the risk interval time for judging the attention dispersed is set to be 30s; if the rule violation is judged, judging whether the driver is not in the driving position, the tire pressure, the overspeed, the shielding, the infrared blocking and the simultaneous separation of the two hands from the steering wheel, wherein the risk interval time of the rule violation is set to be 20s; if the collision risk is judged, it is possible to judge whether the driver has a vehicle forward collision, lane departure, too close a vehicle distance, a pedestrian collision, frequent lane change, a blind area collision, sudden acceleration, sudden deceleration, a sudden turn, or the like, wherein the risk interval time for judging the collision risk is set to 50s. Each risk score category is respectively provided with a score, such as yawning, smoking and the like, which are set to be 1 score, and vehicle risk scores are set to be more than 2 scores to be first-level artificial risks, and more than 3 scores to be second-level artificial risks and the like. And the risk level can be classified according to the artificial risk level, and if the artificial risk level is a second level, the risk level is the second level at the moment.
Referring to fig. 2, in an embodiment of the present application, a risk dividing apparatus based on vehicle safety includes:
the acquisition module 100: for acquiring vehicle data;
finishing module 200: the vehicle data are uniformly arranged and stored;
the judgment module 300: judging whether risks exist according to the tidied vehicle data;
the dividing module 400: for classifying risk if there is a risk.
The application discloses a risk classification method based on vehicle safety, which comprises the steps of acquiring vehicle data through an acquisition module 100 and uniformly classifying and storing the data through a classification module 200, evaluating factors possibly generating risks, judging whether risks exist according to the data classified by a judgment module 300, determining risk categories such as personnel, property and the like possibly involved, searching personnel, equipment and other information possibly exposed to the risks, and quantifying risk classification level through a classification module 400. According to the application, the risk factors possibly appearing are evaluated by combining the data of the vehicle, the traffic safety data, the artificial risk data and the like, and the risk level can be accurately evaluated from multiple aspects in the process of classifying the risk levels according to the preset data and monitoring the behaviors of the vehicle and the driver in real time to judge whether the vehicle and the driver have risks or not, so that potential traffic accidents are effectively avoided.
Further, the acquiring module 100 includes:
the multi-source acquisition unit is used for acquiring vehicle data based on a multi-source acquisition method;
and the vehicle data unit is used for the vehicle data at least comprising vehicle self data, vehicle running preset line data and driver behavior monitoring data.
Further, the finishing module comprises:
the processing unit is used for carrying out data standardization processing on the vehicle data to obtain standardized data;
and the storage unit is used for uniformly storing the standardized data.
Further, the judging module includes:
an analysis unit configured to analyze the vehicle data;
and the first judging unit is used for judging whether the risk exists according to the analysis result.
Further, the dividing module includes:
a first acquisition data unit for acquiring vehicle physical attribute data and vehicle state data based on vehicle itself data;
a first detection unit configured to detect whether a failure rate of the vehicle physical attribute data and the vehicle state data exceeds a threshold value;
a first determination unit configured to determine that there is a risk of the vehicle itself if the threshold value is exceeded;
the first risk classification unit is used for classifying risk grades according to preset risk grades of the vehicle.
Further, the partitioning module further includes:
an acquisition position unit for acquiring a current position of the vehicle based on the positioning device;
the second acquisition data unit is used for acquiring vehicle running preset line data based on the navigation information;
the second detection unit is used for detecting whether an accident is found on a running preset line of the vehicle;
the second judging unit is used for judging that the traffic safety risk exists if an unexpected event occurs;
the second risk classification unit is used for classifying risk grades according to preset risk grades of unexpected events.
Further, the partitioning module further includes:
the third data acquisition unit is used for acquiring the driver behavior monitoring data in real time based on the face recognition device;
a third detection unit for judging whether abnormal behavior occurs in the driver behavior monitoring data;
the third judging unit is used for judging that the artificial risk exists if abnormal behaviors occur;
and the third risk classification unit is used for classifying risk grades according to preset abnormal behavior grades of the driver.
Referring to fig. 3, an embodiment of the present application further provides a computer device, and an internal structure of the computer device may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The nonvolatile storage medium stores an operating device, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing a vehicle database or the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. Further, the above-mentioned computer apparatus may be further provided with an input device, a display screen, and the like. The computer program is executed by a processor to realize a flipped image text recognition method, comprising the steps of: acquiring vehicle data; the vehicle data are uniformly arranged and stored; judging whether risks exist according to the sorted vehicle data, and if so, classifying the risk grades. It will be appreciated by those skilled in the art that the architecture shown in fig. 3 is merely a block diagram of a portion of the architecture in connection with the present inventive arrangements and is not intended to limit the computer devices to which the present inventive arrangements are applicable.
An embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vehicle security-based risk classification method, including the steps of: acquiring vehicle data; the vehicle data are uniformly arranged and stored; judging whether risks exist according to the sorted vehicle data, and if so, classifying the risk grades.
It is understood that the computer readable storage medium in this embodiment may be a volatile readable storage medium or a nonvolatile readable storage medium.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present application and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the application.
Claims (10)
1. A vehicle safety-based risk classification method, comprising:
acquiring vehicle data;
the vehicle data are uniformly arranged and stored;
judging whether risks exist according to the tidied vehicle data;
and if the risk exists, classifying the risk grade.
2. The vehicle safety-based risk classification method according to claim 1, wherein the step of acquiring vehicle data includes:
acquiring vehicle data based on a multi-source acquisition method;
the vehicle data includes at least vehicle itself data, vehicle travel preset line data, and driver behavior monitoring data.
3. The vehicle security-based risk classification method according to claim 2, wherein the step of uniformly organizing and storing the vehicle data comprises:
carrying out data standardization processing on the vehicle data to obtain standardized data;
and uniformly storing the standardized data.
4. The vehicle safety-based risk classification method according to claim 2, wherein the step of judging whether there is a risk based on the collated vehicle data includes:
analyzing the vehicle data;
judging whether risk exists according to the analysis result.
5. The vehicle safety-based risk classification method according to claim 2, wherein the step of classifying the risk if there is a risk includes:
acquiring vehicle physical attribute data and vehicle state data based on vehicle own data;
detecting whether the reject ratio of the vehicle physical attribute data and the vehicle state data exceeds a threshold value;
if the threshold value is exceeded, judging that the risk of the vehicle exists;
and classifying the risk grades according to the preset risk grades of the vehicles.
6. The vehicle safety-based risk classification method according to claim 2, wherein the step of classifying the risk if there is a risk includes:
acquiring the current position of the vehicle based on the positioning device;
acquiring vehicle running preset line data based on navigation information;
detecting whether an unexpected event is found on a running preset line of the vehicle;
if an accident occurs, judging that traffic safety risks exist;
and classifying the risk levels according to the preset risk levels of the accidents.
7. The vehicle safety-based risk classification method according to claim 2, wherein the step of classifying the risk if there is a risk includes:
acquiring driver behavior monitoring data in real time based on a face recognition device;
judging whether abnormal behaviors occur to the driver behavior monitoring data;
if abnormal behaviors occur, judging that artificial risks exist;
and classifying the risk grades according to the preset abnormal behavior grades of the driver.
8. A risk dividing device based on vehicle safety, comprising:
the acquisition module is used for: for acquiring vehicle data;
and (3) a finishing module: the vehicle data are uniformly arranged and stored;
and a judging module: judging whether risks exist according to the tidied vehicle data;
the dividing module: for classifying risk if there is a risk.
9. A computer device, characterized in that it comprises a processor, a memory and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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| CN202310913135.6A CN117172523A (en) | 2023-07-24 | 2023-07-24 | Risk division method, device, equipment and medium based on vehicle safety |
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| CN202310913135.6A CN117172523A (en) | 2023-07-24 | 2023-07-24 | Risk division method, device, equipment and medium based on vehicle safety |
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